


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase 							=  {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  ["LongformerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LongformerForMaskedLM",
          "LongformerForMultipleChoice",
          "LongformerForQuestionAnswering",
          "LongformerForSequenceClassification",
          "LongformerForTokenClassification",
          "LongformerModel",
          "LongformerPreTrainedModel",
          "LongformerSelfAttention",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLongformerForMaskedLM",
          "TFLongformerForMultipleChoice",
          "TFLongformerForQuestionAnswering",
          "TFLongformerForSequenceClassification",
          "TFLongformerForTokenClassification",
          "TFLongformerModel",
          "TFLongformerPreTrainedModel",
          "TFLongformerSelfAttention",
      ]


if TYPE_CHECKING:
      from .configuration_longformer import (
          LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LongformerConfig,
          LongformerOnnxConfig,
      )
      from .tokenization_longformer import LongformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_longformer_fast import LongformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_longformer import (
                LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                LongformerForMaskedLM,
                LongformerForMultipleChoice,
                LongformerForQuestionAnswering,
                LongformerForSequenceClassification,
                LongformerForTokenClassification,
                LongformerModel,
                LongformerPreTrainedModel,
                LongformerSelfAttention,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_longformer import (
                TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLongformerForMaskedLM,
                TFLongformerForMultipleChoice,
                TFLongformerForQuestionAnswering,
                TFLongformerForSequenceClassification,
                TFLongformerForTokenClassification,
                TFLongformerModel,
                TFLongformerPreTrainedModel,
                TFLongformerSelfAttention,
            )

else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




